Energy Aware Computing in Cooperative Wireless Networks

Transcription

1 Energy Aware Computing in Cooperative Wireless Networks Anders Brødløs Olsen, Frank H.P. Fitzek, Peter Koch Department of Communication Technology, Aalborg University Niels Jernes Vej 12, 9220 Aalborg Øst, Denmark {abo ff Abstract In this work the idea of cooperation is applied to wireless communication systems. It is generally accepted that energy consumption is a significant design constraint for mobile handheld systems. We propose a novel method of cooperative task computing by distributing tasks among terminals over the unreliable wireless link. Principles of multi processor energy aware task scheduling are used exploiting performance scalable technologies such as Dynamic Voltage Scaling DVS. We introduce a novel mechanism referred to as D 2 VS and here it is shown by means of simulation that savings of 40% can be achieved. I. INTRODUCTION By nature wireless communication is error prone and great efforts are spend to reduce the impact of these errors. Cooperation methods have been introduced as a mean for compensating some of these issues. The basic principle of sharing resources for diversity is used. In [1] a number of physical layer cooperative issues are surveyed, and in broad terms relaying concepts is the main philosophy. Examples are i detect and forward, ii amplify and forward, and iii. coded cooperation. In [2] cooperative networks at higher layers are discussed, outlining the cooperative network architecture. The key points include i systems should be layered on demand, ii. reuse of module blocks, iii. multiple services, and iv. end-to-end connectivity across access technologies. From a conceptual view point cooperative networks is introduced to get a better performance within the network, where issues like capacity, spectral efficiency, bandwidth, reliability, and even security may be improved. Cooperative terminals are equipped with a short range air interface technology and maybe also with a centralized air interface in the cellular case. In Figure 1 the principle concept of cooperation within a cellular network is illustrated. Fig. 1. Principle of cooperation between terminals with a cellular network. In next generation mobile wireless systems, referred to as fourth generation 4G, new envisioned multiple services will cause an increasing algorithmic complexity. This complexity increases the price of the terminal as more and faster hardware is needed, eventually increasing the energy consumption and thereby reduces the operational time of the device. Both the price and operational time or stand-by time are among the most important criteria for the customers decision to buy a terminal. In this work, it is therefore investigated under which conditions cooperative computing of task sets can result in energy savings. The principle approach is to i. employ various scheduling methods and optimizing cost functions for assigning tasks to multiple terminals, ii. distribute the tasks using the short range wireless network, and iii. reduce energy consumption on the individual terminals. Our proposed method is an abstraction of the multi processor architectures. Terminals are considered as processing units, and their short range wireless communication is the inter processor communication. When introducing wireless links between processing units communication cost will become significant. This means that cost for transmitting/receiving the task should be carefully considered. Task allocation and scheduling are well known topics for multi processor architectures. Energy control is important for the terminals, and for single processors Dynamic Voltage Scaling DVS has proven to be a near optimum energy scheduling of task sets [3]. Based on the energy conserving qualities of DVS, together with the abstraction into multi-processor environment, we propose our method of Distributed DVS or D 2 VS for short. To the best of our knowledge only one attempt on cooperative computing for sensor networks [4] is made. In this work an ILP-based approach is made for an epoch based real time applications, with the results that system operational time is improved by a factor of one magnitude in the best case. In [4] a relatively narrow formulation is used, whereas we are aiming for a more broad specification, and secondly bases our approach on a more dynamic environment. The reaming of the paper is organized as follows: In Section II a motivating example is presented. In Section III the problem of energy aware computing in cooperative wireless networks is formulated. In Section IV explains energy awareness by dynamic voltage scaling. In Section V simulation results are shown and finally in Sections VI the conclusions

2 and future work is discussed. Multi-processor Architecture II. COOPERATIVE SCENARIO EXAMPLE Before formulating our D 2 VS scheme in detail we introduce an example scenario to illustrate the importance of cooperation. Imagine a multiple description coding MDC example, where three streams has to be received from a central network, decoded and forwarded to a sink using a short range link as given in Figure 2. Task-set DVS Scheduler Network Architecture S Cost 1» 3R + 1UT 1» 3D 1» 3F S Cost 3» R + UT + R + UT + R + UT 3» 1D 3» F X S Cost 1» 3R + 1UT 3» D + D + D 3» 3F + R + F + R + F X Centralized Link, Short Range Link, R Receive, UT Up Time, F Forward, D Decode Fig. 2. Multiple description coding example of task distribution among terminals for cooperative execution using centralized links to receive the task and short range links to forward the decode result. A terminal includes a centralized link, a processing unit, and a short range link. Under the assumption that the short range link is cheap in terms of energy consumption, the central link is expensive when receiving and operating. Furthermore, the decoding has to be done under real time constraints. Under these assumptions the following observation is made: In the first case a single terminal is receiving all three streams, decoding and forwarding them to the source. For the wireless links this implies three active periods and the processor must be able to decode all three streams within the period. In the Second case three terminals are used to receive one of the three MDC streams. After that the streams are decoded and forwarded. The wireless links handle only a single stream and the processor also decode only on stream. Energy wise, the difference is that two additional sets of wireless links are active, but the processor only decodes one stream, implying it can run at one third speed. In the third case one central link is active and one terminal forward streams to two other terminals over the short range link. Each terminal still decode and forward one stream to the source. The advantage being that two central links can be idle, though introducing additional traffic on the short range link. As the short range link is considered energy cheap the third case will have a potential energy gain compared to the second case. Which one of the three cases is most energy efficient depends on the wireless technologies used. III. COOPERATIVE ENERGY AWARE COMPUTING Figure 3 illustrates the overall abstraction of the energy aware cooperative terminals. An abstraction of multi processor environments containing a number of processing Fig. 3. Abstraction of a multiple processors environment, connected by abstract network architecture. A DVS method schedules the task-set onto the multi-processor architecture and performs energy conservation. units interconnected by a certain network topology is used. Mobile terminals are modeled as processing units and the interconnecting network represents a short range wireless communication technology. Efficient energy reduction methods conserve energy on the individual terminals, where the DVS method is used because of its efficiency. From energy aware multi processor scheduling it is known that a number of slow operating processing units have an energy advantage compared to a single fast unit. This effect is discussed in Section IV-C. The principle approach, as stated in the introduction, is to: i. Employ various scheduling methods and optimizing cost functions for assigning tasks to multiple terminals. ii. Distribute the tasks using the short range wireless network. iii Reduce energy consumption on the individual terminals. The overall concept of cooperative computing is only valid if: The mobile terminals cooperating on energy aware task execution must individually gain from the cooperation, and are therefore willing to exchange task sets. If not fulfilled, terminals act selfishly and execute their task by them self. In the following the D 2 VS parts are formulated. A. Application As shown in Figure 1 and also discussed in Section II the application is introduced by a reception of information. This received data then needs to be processed on the terminal starting a series of execution events. These are abstracted into a real time task set specified by: Arrival time: At a given time the task becomes active indicated by its arrival time. Deadline time: The deadline determines the time where the execution of the task must finish. Arrival time together with deadline time determines the task period. Depending on the real time constraints task are executed within there period. Real time is specified as by Figure 4. Workload: A task introduces a workload on the processor. This is specified in worst case execution time WCET or worst case execution cycle WCEC. The actual functionality of a task is of no importance for the processing unit, workload determines the processor time that is spend for executing the task.

3 Utility value Utility value Utility value R D Time R D tv Time R D Time A Non RTS B Soft RTS C Hard RTS Fig. 4. A is a system without time constraints, meaning that the task has the same quality at all times. B is a system with soft constraints, where the task quality decay as times goes by. C is a hard real time system where exceeded execution result in corrupted task execution. In order to distribute tasks parallelism within the task set is important. Dependencies between tasks is another measure indicating the relation between tasks. For fully independent task sets the number of feasible processors is determined by the number of tasks. For the remaining of this work only independent task sets are consider. B. Distribution of tasks The important part of the D 2 VS scheme is the decision of what and how many tasks can be or should be distributed among terminals. The decision must be founded on cost models of executing task i. local, ii. remote, and very important iii. the cost of transmitting the task. The task set is considered to belong to a given terminal referred to as the local terminal. Terminals receiving tasks from the local one are noted remote terminals and for simplicity only a single remote terminal is considered in this work. First, the decision of the ratio between local and remote task execution is expressed as: E L m i=1 > E L j i=1 + E N m i=k + E R m i=k where is the i th task of the m tasks, and E is energy consumption operators. The inequality state that energy for executing the entire task set at the local unit must be greater than executing some of the task set local and the remaining part at the remote terminal. Additionally, the overhead of distributing the tasks also must be considered. The energy operators; E L, E N, E R are the energy at the local terminal, energy on the network, and energy at the remote terminal, respectively. A further improvement can be made if the distribution is fulfilled using an adaptive principle, where the size of the task part for remote execution is varied according to system conditions. This could be beneficial if the network quality is poor, or if a terminal offers to execute a larger part of the task-set. Exact proposals for distribution mechanisms are not within the scope of this work as it is assumed that tasks equally will be split among the local and remote terminal. C. Network transmission For our scheme a short range technology is important, mainly in sense of energy consumption. This is obtainable as the technology only has to cover a short physical distance. From known technologies like e.g. Bluetooth there is a support of adjustable power emission on the air interface according to distance. A technology like Wireless LAN is typically not 1 energy aware and therefore a poor candidate as short range link. In Figure 1 a cellular example is illustrated, needing a centralized and a short range technology for local cooperative communication. Future technologies may be able to handle both centralized and short range communication using the same technology. When distributing tasks overheads on the short range network will occur. The task introduces a load on the network, determined by the capacity and by the size of the task. These parameters define the active time for the network and thereby the network overhead. This overhead will occur both when the task is distributed to a remote terminal and when the computed data is returned. Eventually, the active time is responsible for the communication overhead. This lead to data compressions schemes to reduce the cost for the transmission on the wireless link, but will lead to additional computations on the other side. Therefore the compression schemes have to be chosen wisely. D. Task Execution When the tasks eventually are assigned to the individual terminals they are executed. Reducing energy on the local and remote terminal is the goal of our method. DVS is utilized because of its superior energy conservation. Allowing for the introduction of overheads when distributing tasks onto remote terminals. The principle of DVS and its efficiency is explained in the following section. IV. ENERGY AWARENESS For battery powered systems, like handheld portable wireless terminals, methods for energy reduction has become a huge research topic within the last decades. The recent advantages in embedded processors are the support for dynamic changes to the clock frequency and supply voltage, a technology referred to as speed scaling. Dynamic Voltage Scaling DVS utilizes the capability of changing the circuitry clock frequency and supply voltage. DVS is a resource scheduling scheme, where knowledge of the application task-set is utilized to scale the performance of the processor. DVS can be seen as an extension to traditional power management, where different power down modes is utilized. DVS is an extension to this, as multiple stages in the active stage exists. A. Sources of power consumption The effectiveness of DVS is to be found in circuitry power consumption. The power dissipation of typical embedded implementation technology, which is CMOS, contain of three components: P total = P dynamic + P leakage + P short 2 P dynamic being the dynamic power, related to the switching activity within circuit gates. P leakage being the constant flow of current flowing through the reversed-biased diodes within the transistors. P short being the direct path from voltage supply to ground, whenever the P- and N-MOS transistors

4 make a level transition. The most significant contribution relates to the dynamic power: P dynamic = K C L V 2 dd f 3 where K is the activity factor of the circuitry, C L the equivalent load capacitance, V dd the supply voltage, and f is the clock frequency. Another important physical parameter is the circuit delay, which determines the obtainable frequency of the unit. V dd T delay = k V dd V th α 1 4 V dd where k and α are technology constants, and V th is the threshold voltage. The time delay is typically considered proportional to the inverse of the supply voltage, meaning that the lower V dd, the higher delay and thereby the lower obtainable clock frequency. As power consumption is a convex function dominated by the squared supply voltage, the effectiveness is found by lowering the voltage which, however, is only possible if joint scaling of the frequency is made. This implies that optimal pairs of clock frequency and voltage supply are present, where such pairs are referred to as a speed. B. Dynamic Voltage Scaling Over the last years numerous DVS methods have been proposed, basically categorized into two groups; Intra- and Inter-task DVS methods [5], where the most investigated and practicable is Inter-task DVS. Inter-task DVS methods are working in a task by task manner, typically based on traditional scheduling policies, such as Fixed Priority FP or Earliest Deadline First EDF. Speed scaling is conducted using taskset knowledge, adjusting the speed according to the present performance needs on the processor [6], [7], [3], [8]. To get an understanding of task scaling, an example is illustrated in Figure 5 the bobble. The task is specified by i. arrival time, ii. deadline time, and iii. workload. The F clk, Vdd1 Full speed w i IDLE overall idea is to utilize task period by adjusting the speed of the processor and thereby gaining an energy saving. Also in Figure 5 a simple example of a task set is illustrated, containing two tasks with periodic arrival frequency. Without DVS, slack time is introduced and often handled as an idle task. The idea of the Inter task DVS methods is to convert the slack time into energy savings by lowering the processor speed. Stretching or prolonging the execution time of the task, while maintaining the overall task-set timing constraints, is made. Illustrated in Figure 5 by the DVS scheduling policy proposed by [6]. Most proposed methods are for single processor architectures, where the contributions for multi-processor architectures are significantly less. The simple scheduling policy is to balance the workload on the processors and speed scale the system [9]. C. Effectiveness of DVS In [10] we showed that task set specification and hardware specification must be carefully considered. The effectiveness of DVS therefore depends very much on HW and SW parameters. We argue that multiple processors operating on a slow speed is having an energy gain compared to a single fast one, which should be obvious from Section IV-A. The resent trend in the processor marked is also pointing towards multiprocessors or multi-core architectures. Traditionally, multiprocessors are used for performance enhancement, where the resent re introducing seems more as a mean for energy reduction. By using multi-processors methods for performance enhancement, systems with high performance and reduced energy consumption are obtainable by having a number of low energy consuming slow units. In Figure 6 measurements of a modern DSP facilitated with speed scaling technology are illustrated. Power consumption for a single unit from 1/1 speed to 1/4 speed is shown, indicating more than a factor 3 power reduction going from 1/1 to 1/2 speed. Also in Figure 6 power consumption for multiple units is illustrated. With the assumption that; i. identical load is processed on the systems, ii. workload can be divided equal at the processing units, and iii. that the inter processor communication is cost free. The power reduction of [mw] Scaled speed 275 F clk 2, V dd2 w i Task-set Arrival Deadline Multiple Units No-DVS 80 DVS Single Unit Fig. 5. A task-set of two tasks with a periodic arrival. A traditional schedule indicating that slack time is introduced by the tasks-set, implying idle time for the processing unit. A DVS schedule stretches the execution time of the task by speed scaling, utilizing available time. In the bubble: a single task defined by its parameters, showing that task is utilized by scaling Speed Units Fig. 6. Power measurements of an Analog Devices Blackfin processor BF533. Also showing the effect of multiple slow operating units

5 having 2 units instead of one is approximately a factor 1.7, for this specific processor. It is also seen that going to 3 and 4 units do not have any positive effect, 4 units even consumes more power than 2. This indicates a maximum on the processing units. Comparing to a single unit the multiple units is clearly having an energy advantage, assuming identical performance and utilization loads for the systems. V. SIMULATION RESULTS The overall goal of the simulations is to account for the effectiveness of the proposed concept. In order to do so, some assumptions for the system model are first made. Following are two experiments showing the effect of network energy and load. Finally the effect of error prone communication is investigated. A. Simulation Setup In these experiments two terminals are used as illustrated in Figure 7, with the assumption that each terminal is assigned equal parts of the task-set. Task-set Multi-processor Architecture DVS Local DVS Remote Fig. 7. Experimental setup. Using two terminals cooperating on task execution, each scheduled by DVS methods for energy conservation. For the experiments a task-set proposed in [6] is used, specified in Table I. In principle, the two terminals will execute identical task-sets, with the overall same timing constraints and workload, though with a displacement in time for at the remote terminal. The reason for this is the important overhead introduced by the network distributing the tasks. TABLE I EXPERIMENTAL TASK-SET SPECIFICATION, INDICATED IN TIME UNITS Arrival Deadline workload The energy consumption introduced by the wireless link is of great significance and modeled using the following assumptions: i. The network is capable of transmitting and receiving the tasks when the processing units execute tasks. ii. The load on the network is modeled as a percentage of the task workload. As an example, if the workload is 1 unit and the network load is specified to 25%, the network overhead becomes 0.25 time units. The network overhead is both introduced when transmitting the task to a remote terminal and also when it is received from the remote terminal. The energy consumption of the network is constant when active, represented by an energy value, and zero when inactive. In the experiments the two terminals are scaled by the well known DVS algorithm proposed by [6]. Each terminal energy consumption is model by a simple square speed relation, which is an often used model from DVS literature. The terminals maximum speed is one time unit, implying that the maximum energy consumption is also one. It is assumed that each terminal s power consumption in the idle state is zero. B. Network energy overhead The experiment evaluates the effect of the network overhead. For reference the task-set is executed on a single terminal without and with DVS, using the reference tasks-set from Table I. Without DVS at a single processor: [Energy] With DVS at a single processor: [Energy] When the task-set is executed without DVS an ideal power management is assumed, placing the processor in a zero energy state whenever the processor idles. From the example it is clearly seen that DVS result in superior energy consumption, reducing the energy by more than a factor two. In the experiments the energy of the network link is varied using three values {1, 0.5, 0.1} and network loads of 5% and 20%. In Table II the results are shown, both without and with DVS. In the table the ratio between a single terminal and the D 2 VS approach is indicated as an increase or decrease of energy consumption related to the single terminal reference, respectively without and with DVS. TABLE II SIMULATION RESULT WITH NETWORK ENERGY WITH VALUES OF {1, 0.5, 0.1} AND TASK LOAD ON THE NETWORK OF 5% AND 20%. THE NUMBER IN BRACKETS INDICATE THE RATIO TO A SINGLE TERMINAL. Network Energy 1.0 Network load 5% Network load 20% Without DVS With DVS Network Energy 0.5 Network load 5% Network load 20% Without DVS With DVS Network Energy 0.1 Network load 5% Network load 20% Without DVS With DVS From Table II it is seen that without DVS no energy gain of distributing tasks among terminals is present. This is obvious, as an overhead for distributing tasks to the remote terminals is introduced and as no effective energy conservation is present at the terminal. More interesting is the results with DVS. From Table II it is clear that network overhead is affecting the performance of the proposed method. Comparing to the single terminal reference, the results for task-set load of 5% shows that our proposed method gain savings of 20%, 30% and 39%, respectively to the three network energies. For taskset loads of 20%, it is only when the network energy is 0.1 an energy gain is present; in the two other experiments the energy consumption exceeds the single terminal. These results show that when scheduling task onto remote terminals

6 the network cost must be careful considered, as the energy consumption of the network will cause increases in the total energy consumption. C. Error Prone Network In wireless systems network links often are error prone, meaning that the information is lost by a given probability. In these simulations the task distributed is given a probability of success when transmitting. If the information is lost it must be re transmitted, introducing additional load on the network link. In the experiment the same task-set as before is used, with network load of {5%, 10%, 15%, 20%}. The network energy is chosen to 0.5 in the experiments. In Figure 8 it is seen that the rate of successful transmission on the short range link has a significant impact whether to cooperate or to perform the task locally. The horizontal line in the plot is the single terminal reference. In case of 20% network load the energy consumption exceeds the single terminal reference and will therefore not be beneficial to perform it in a cooperative manner. In case of 15% and 10% network load the energy consumption crosses the single terminal reference as the success rate drops. In these two examples the proposed method will only be beneficial for success rates of more then 0.8% and 0.55% respectively for the two network loads. In case of 5% network load the energy consumption is below the single terminal reference and it is seen that only slide effects of the success rate is present. The energy overhead introduced by distributing tasks is in this case less significant and therefore not affecting the network energy overhead significantly, as compared to the other three cases. Abstracting these results the success rate of communication can be seen as an adaptive network load, which will increase the energy overhead depending on the success rate. Therefore in environments with low success rates, network energy consumption must be relative low, either the load caused by the task or the active energy consumption of the network. Normalized Energy Network load 5% Network load 10% Network load 15% Network load 20% Single Terminal Probability of Success Fig. 8. Simulation results of task-set from Table I, with network load of {5%, 10%, 15%, 20%}, and network energy 0.5. Transmission is defined by a probability of success, meaning that 1 is 100% probability that the information is successful transmitted VI. CONUSIONS AND FUTURE WORK In this work we have presented an approach of cooperating on task execution. The approach is referred to as Distributed DVS or simply D 2 VS. The method is based on knowledge of traditional multi processor scheduling and dynamic voltage scaling principles. It is assumed that the task set has inherent parallelism allowing task distributed among processing units. The effectiveness for the proposed scheme was investigated by means of simulations. Within the simulations the scenario of only two cooperating terminals has been investigated. It is shown that DVS is introducing the superior energy conservation on the individual terminals. Overall the simulation results show that energy savings of 40% can be achieved. Furthermore it is shown that network energy, the overhead, and the error probability of the short range communication introduced by the task have a significant importance. This imply that careful decision on whether to distribute a task must be made, according to task load, network energy and the probability of successful transmission. The next step is to device the mechanism that, based on cost functions, deciding if a task should be distributed. In this work we showed that by blindly distributing tasks energy saving is possible, a selective distribution function can most likely further improve this saving. 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