Overcoming the Energy versus Delay Trade-off in Cloud Network Reconfiguration

Transcription

1 Overcoming the Energy versus Delay Trade-off in Cloud Network Reconfiguration Burak Kantarci and Hussein T. Mouftah School of Electrical Engineering and Computer Science University of Ottawa Ottawa, ON, Canada Abstract Cloud computing calls for efficient solutions to manage the energy consumption of the transport, process and storage services. Recently, we have shown that energy savings in the cloud network and the data centers are at the expense of increased delay; hence degraded service quality. In this paper we propose a new scheme, Delay and Power Minimized Provisioning (DePoMiP) to address energy versus delay tradeoff in the cloud network. DePoMiP reconfigures the cloud network and provisions the demands by jointly minimizing the energy consumption and propagation delay. Through simulations, we compare DePoMiP to our previously proposed heuristics for delay-minimized provisioning and power-minimized provisioning of the demands. Simulation results show that DePoMiP mimics power-minimized provisioning in terms of power consumption while it provisions the demands with a few microseconds higher propagation delay when compared to delay-minimized provisioning. Furthermore, its low channel utilization in the IP over WDM transport network, as well as its fairness among the nodes in terms of power consumption, makes DePoMiP a promising solution for the problem of energy-efficient reconfiguration of the cloud network. Keywords-Cloud computing, data centers, energy-efficiency, IP over WDM, RFWA, virtual topology I. INTRODUCTION With the advent of cloud computing, infrastructure, software, storage and platforms are provisioned as services in an on-demand manner with respect to the utility computing fashion [1]. Cloud computing is defined as a pay-as-you-gobased business model that serves as an umbrella over many distributed and high performance computing systems [2]. Energy management is one of the significant challenges in cloud computing along with automated service provisioning, virtual machine migration, server consolidation, security, novel architecture design and energy management [2]. Recently, various studies have focused on energy-efficient and/or green design of telecommunication networks, and 40% of the Greenhouse Gas (GHG) emissions of the telecommunication networks have been reported to be due to the utilization of local resources such as personal computers, monitors and so on [3]. Therefore, by moving the services to remote locations and considering server consolidation and virtual machine migration, significant energy savings can be achieved. On the other hand, moving the services into the cloud will increase the associated transport energy [4]. Besides, data centers that host the cloud services are forecasted to contribute to 3% of the electricity consumption only in the U.S [5] while a significant amount of this ratio is due to non-it equipments such as cooling and uninterrupted power supplies. Recently, cooling and thermal-aware job scheduling solutions [6], [7], [8], have helped enhancing the energy-efficiency of the data centers. Yet, energy-efficiency of the transport medium needs to be addressed. In this paper, we study energy-efficient reconfiguration of the cloud network and demand provisioning problem in order to address the energy versus delay trade-off. We propose a new scheme called Delay and Power Minimized Provisioning (DePoMiP). Assuming that the demand profile can be forecasted ahead of time [9] and each backbone node is associated with a data center, DePoMiP initially aims at routing the demands over the virtual cloud network backbone, then the virtual links of the network are mapped onto the physical topology. As a compromise between power savings and propagation delay, DePoMiP formulates the link costs for Routing and Fiber and Wavelength Assignment (RFWA) configuration by including both delay and power consumption objectives. Furthermore, when determining the destination data centers for a submitted job, DePoMiP aims at selecting the data centers with least power consumption and those will lead to the least propagation delay. We compare DePoMiP to its predecessors, delay-minimized provisioning and power-minimized provisioning schemes which have been proposed in [10]. Our simulation results show that DePoMiP can ensure significant power savings in the cloud network without increasing the channel utilization level of its predecessors. Furthermore, DePoMiP fairly distributes the power consumption among the backbone nodes, and it introduces only a few microseconds of additional propagation delay to the delay-minimized provisioning. The paper is organized as follows. Section II presents related work on energy-efficient transport of cloud services. Section III presents the proposed solution, DePoMiP in detail, which is evaluated through simulations in Section IV. Section V concludes the paper and gives future directions. II. RELATED WORK Job submissions in cloud computing are handled with anycast or manycast paradigms borrowed from grid computing, where anycast refers to selecting a single destination out of a set of candidate destinations [11] while in manycast, a subset of destinations is selected from the set of candidate destination nodes. Manycast is a derivative of multicast communication mode which is denoted by the tuple, (s, D), /12/$ IEEE

2 while manycast is denoted by the tuple, (s, D c D) where s, D and D c denote source, set of eligible destination nodes and the subset of eligible destination nodes, respectively [12]. Energy-efficient design of the backbone for Internet and data center demands has been studied in [11] over the IP over Wavelength Division Multiplexing (WDM) transport medium. In the corresponding study, the authors have proposed the optimal locations of a limited number of data centers in the transport network. Once the data centers have been located, a demand provisioning algorithm has been proposed to reconfigure the network by virtual topology mapping. In the proposed provisioning algorithm, regular Internet traffic and the downstream traffic originated from the data centers are provisioned based on unicast routing while upstream data center (DC) demands carrying the job submissions are provisioned with respect to the anycast paradigm. In [13], the authors have proposed an optimization model where energy-efficiency is provided by switching off the appropriate network elements and serving the demands with respect to anycast routing. In [14], we have proposed an evolutionary algorithm for energy-efficient provisioning of manycast demands over wavelength-routed transport network. In [15], we have proposed Mixed Integer Linear Programming (MILP) formulations for energy-efficient cloud network design. Those MILP formulations have been later used as benchmark schemes for two heuristics which we have called the Delay Minimized Provisioning (DeMiP) and the Power Minimized Provisioning (PoMiP) [10]. In [10], we have shown that energy savings in the cloud network are at the expense of increased propagation delay. III. DELAY AND POWER MINIMIZED PROVISIONING (DEPOMIP) IN THE CLOUD NETWORK A. System model and assumptions Figure 1.a illustrates an overview of the system model. IP over WDM network is the transport medium, and each backbone node is associated with a data center where processing and storage facilities are hosted. Each data center is linked to its associated backbone node via a gateway node. End users receive cloud services through the metro and access networks. Figure 1.b zooms into the backbone and presents the main components of an IP over WDM link. The figure further explains the idea of optical bypass. Demands are routed in the IP layer while transmission is done in the WDM layer. To assure energy-efficiency, IP routers have to be bypassed and the traffic should be kept in the optical domain as much as possible since power consumption of an IP router port is around fourteen times the power consumption of an optical transponder [11]. Besides the above mentioned points, the following assumptions are made prior to presenting the design specifications of DePoMiP. Three types of traffic is transported over the backbone, i.e., downstream traffic originating from (a) (b) Figure 1. The system model (a) An illustration of a public cloud, (b) An IP over WDM link in the backbone. data centers, upstream traffic destined to a group of data centers (i.e., manycast) and the regular Internet traffic. For each data center, power consumption overhead that would be introduced by a job submission is known in advance. Power consumption and delay introduced by the aggregation routers and the data center gateway are neglected. Regular Internet demand profile is forecasted for three-hour timeslots throughout the day. Figure 2 illustrates virtual topology mapping in the IP over WDM network. Dashed links in the IP layer form the virtual topology while physical medium that transports the demands is the WDM network. Thus, each virtual link in the IP layer is mapped onto a lightpath in the WDM layer. B. DePoMiP DePoMiP adopts the motivation of [11] where multihop optical bypass concept has been employed in order to reduce the power consumption by bypassing the IP routers. Algorithm 1 presents a pseudocode for the heuristic which starts provisioning the list of demands, DList, in decreasing order with respect to their size. Downstream data center (DC) and regular Internet demands are recognized as unicast /12/$ IEEE

3 Figure 2. Virtual topology mapping in the IP over WDM network demands. It is worth to note that, downstream DC traffic is aggregated at the backbone nodes; hence downstream DC traffic destined to a node is treated as multiple unicast flows originated from several data centers and are destined to the corresponding node. Upstream DC traffic is provisioned based on the manycast paradigm. Unicast Demands: Downstream DC demands, as well as the regular Internet demands, require unicast provisioning. Since, power minimization is one of the objectives, demands are aimed to be routed over the virtual topology as long as possible. To this end, for a unicast demand from node-s to node-d, a virtual path is searched on the virtual topology by assigning the cost function in (1). In the equation, link mn denotes one of the physical links that form the virtual link-ij where c phy mn is the physical link cost assigned for the corresponding physical link. According to the cost assignment function (c v ij ), DePoMiP aims at selecting the virtual links that lead to minimum total physical link costs and that have available capacity (Λ ij ) to accommodate the incoming demand. If a virtual path cannot be found on the physical topology, a virtual link is added, and the newly added link is routed on the physical topology. c v ij = link mn link ij c phy mn, Λ ij > 0, else Equation (2) is called to assign physical link costs when routing the virtual links over the physical topology where P edfa, and P t denote the power consumption of an Erbium Doped Fiber Amplifier (EDFA) and a transponder, respectively. Besides, S mn and W mn denote the number of EDFAs per fiber and the number of available wavelength channels in link-mn, respectively. Out of the physical links with available wavelength channels, the heuristic aims at selecting the ones that will lead to joint minimization of power consumption per fiber and propagation delay where Lf mn is the length of the physical link-mn. Once, a virtual link is routed over the physical topology, fiber and wavelength assignment is done based on the first-fit policy. It is worth (1) Algorithm 1 DePoMiP in the Cloud Network 1: {DList: List of sorted demands in decreasing order} 2: {D s : Candidate DCs set for upstream demand of node-s} 3: {DC min : Minimum number of data centers to be reached} 4: {d c : List of destination data centers} 5: {I 1 (I 2): Index of minimum ranked data centers w.r.t Rd(R 1 d)} 2 6: {Rmin 1 (Rmin 2 ): Minimum Rd 1 (Rd) 2 value} 7: Begin 8: while (DList is not empty) do 9: if (DList empty) then 10: Goto line 52 11: else 12: Pop the first demand from DList 13: if (Upstream DC demand) then 14: k 0, d 0 15: Rmin 1, I 1, Rmin 2, I 2 16: for (k 0toDC min) do 17: if (k is even) then 18: for (d 0to D s ) do 19: if (Rd 1 <Rmin 1 AND d is not in d c ) then 20: Rmin 1 Rd, 1 I 1 d 21: end if 22: end for 23: d c [k] I 1, Rmin 1 24: k k +1 25: else 26: for (d 0to D s ) do 27: if (Rd 2 <Rmin 2 AND d is not in d c ) then 28: Rmin 2 Rd, 2 I 2 d 29: end if 30: end for 31: d c[k] I 2, Rmin 2 32: k k +1 33: end if 34: end for 35: for (all data centers in d c) do 36: (s, d) source, destination pairs 37: Set the virtual link costs 38: Route on virtual topology 39: if (Routing successful) then 40: Update virtual link capacities 41: else 42: Add new virtual link from s to d 43: Route link on the virtual topology 44: Goto Line 39 45: end if 46: end for 47: else 48: (s, d) (source, destination), d c d 49: Goto Line 37 50: end if 51: end if 52: end while 53: End to note that number of EDFAs per fiber is determined by (3) where Δ span denotes the fiber span length. { } c phy (Pedfa S mn = mn + P t W mn ) Lf mn W mn > 0 else (2) /12/$ IEEE

4 S mn = Lf mn /Δ span +1 (3) Manycast Demands: For an upstream DC demand, De- PoMiP reduces the manycast problem to a lightweight multicast problem by determining the destination data centers out of the set of candidate data centers, D s. Each data center in D s is assigned a ranking value as shown in (4) where Θ s,d denotes the power consumption overhead introduced by the job submitted from node-s to data center-d while DCd cool and DC proc d are current processing and cooling power consumption of data center-d, respectively. In the equation L sd denotes the pre-computed shortest distance from backbone node-s to data center-d on the physical topology. As seen in the equation, DePoMiP computes two different ranking values, Rd 1 and R2 d for each data center-d. As seen in the pseudocode, DC min data centers are selected where DC min /2 destinations are determined based on Rd 1 ranking and DC min /2 destinations are selected based on Rd 2 ranking. R1 d ranking denotes the power consumption overhead that will be introduced to data center-d if the corresponding job is submitted to it while Rd 2 denotes the propagation delay between node-s and data center-d. Θ s,d + DC Rd r d cool = + DC proc d d D s r =1 L sd d D s r =2 else (4) Once the destination data centers are determined, for each data center, (1) is used for virtual link cost assignment, and if a virtual path can be found on the virtual topology, it is accepted for the corresponding data center. If a virtual path cannot be found, then a new virtual link is added between the source node-s and the associated backbone node of the destination data center-d. Equation (2) is used when routing the newly added virtual link over physical topology. However, upon obtaining the physical route, a posterior search is performed on each physical link of the manycast demand as follows. If a physical link is utilized by previously established routes to any destination data center of this manycast demand, fiber and wavelength assignment for the corresponding manycast demand on the corresponding link is used for the physical route of the newly added and routed virtual link. Here, multicast capability of the Optical Crossconnects (OXCs) is used to reduce channel consumption. Remaining links of the physical route are assigned fiber and wavelengths based on the first-fit fashion. These steps are not presented in the pseudocode for the sake of simplicity. Selection of destination data centers takes at most O(N 2 ) where N is the number of backbone nodes in the network. As DePoMiP adopts the multi-hop bypass routing concept [11], its RFWA has a runtime complexity of O(N 4 ) due to virtual topology mapping, which is the complexity of DeMiP and PoMiP, as well. Thus, DePoMiP does not introduce an overhead of runtime complexity to its predecessors. IV. NUMERICAL RESULTS A. Simulation Settings Numerical results are collected under the NSFNET topology where each backbone node is associated with a data center, and a data center is assumed to consume between 168kW (100 kw) of idle processing (cooling) power and 319.2kW (280 kw) of full utilized processing (cooling) power where submitted jobs are placed in the servers based on the Minimizing Heat Recirculation (MHR) fashion [8]. For each demand of the upstream manycast traffic, the number of candidate destinations is either 3 or 4 while the desired number of destinations (DC min )isfixedto2.ajob submission is assumed to increase the data center workload between and 0.2. The aggregated downstream DC traffic destined to a backbone node is assumed to originate from either 2, 3 or 4 data centers. Cloud network is assumed to be initially loaded due to initial data center loads which vary from 0.10 to Besides, we present node power fairness behavior of DePoMiP throughout the day. The NSFNET topology consists of four time zones, and due to having four time zones, demands are heterogeneously distributed in the network. These zones are EST (Nodes: {8,10,11,12,14}), CST (Nodes: {6,7,9}), MST (Nodes: {4,5,13}) and PST (Nodes: {1,2,3}) as illustrated in [11], [14]. Table I INTERNET DEMANDS IN THE NSFNET THROUGHOUT THE DAY (GBPS) Hours Zones EST CST MST PST Regular internet demands are presented in Table I for three-hour timeslots of a day, and at each time slot, upstream and downstream DC demands are assumed to be 0.2 and 1.2 times of the regular Internet demands to be coherent with [11], [14]. Note that timeslots are represented in EST time. When starting a new reconfiguration of the virtual cloud network, the physical network is assumed to be overprovisioned where each fiber consists of 16 wavelengths each of which is operating at 40Gbps. Propagation delay per kilometer in fiber is assumed to be 5μs. Equation (5) presents the total power consumption of the cloud network (P total ) as the sum of the power consumption of the backbone nodes. In the equation, P r and C ij denote the power consumption of an IP router port and the number of active lightpaths in the virtual link-ij, respectively, where the network consists of N nodes, and Ni v denotes the set of neighbors of node-i in the virtual topology. Besides, N p i and f ij denote the set of neighbors of node-i in the physical topology, and the number of active fibers in the physical link /12/$ IEEE

5 ij, respectively. The other parameters have been defined in the previous section. P edfa, P t and P r are assumed to be 8W, 73W and 1000W, respectively [11], [14]. P total = ( P r C ij i N + j N p i j N v i (P t W ij + S ij P edfa f ij )+DC cool i + DC proc i ) Performance of DePoMiP is evaluated in comparison to its predecessors, which are briefly defined below [10]: - Delay Minimized Provisioning (DeMiP) uses the same provisioning method in Algorithm 1. However, when determining the destination data centers of an upstream DC demand, it ranks the data centers based on their physical distances to the source node and selects the ones with the lowest ranking values. Besides, link costs in both virtual and physical routing are assigned based on the physical distances between the nodes forming the links in order to minimize the propagation delay. -Power Minimized Provisioning (PoMiP), as well, consists of the same algorithm steps with DeMiP and DePoMiP. When routing an upstream DC demand, PoMiP ranks each candidate data center based on its current power consumption assuming that the submitted job has been destined to the corresponding data center. As opposed to DeMiP, when routing the demands, PoMiP assigns link costs in the virtual and physical topologies considering the power consumption of the corresponding links. B. Simulation Results Figure 3 compares DePoMiP to its predecessor schemes in terms of power consumption increase in the cloud network. Due to only considering the propagation delay and disregarding the power consumption, DeMiP leads to the maximum power consumption increase among these three schemes. At each timeslot of the day, DePoMiP can introduce an identical performance to PoMiP; hence power minimization is ensured by the proposed heuristic. In Figure 4, the proposed heuristic, DePoMiP is compared to DeMiP and PoMiP in terms of wavelength channel utilization in the network. Since channel utilization of PoMiP and DeMiP coincides, DePoMiP is expected not to increase channel utilization as it aims to mimic the behavior of the two approaches and make a compromise between power savings and propagation delay. Since all schemes adopt the same virtual topology mapping approach, they have similar channel utilization performances. Figures 5.a-c compare DePoMiP to DeMiP and PoMiP in terms of propagation delay for the regular Internet traffic, downstream DC and upstream DC traffic, respectively. PoMiP leads to an additional propagation delay to DeMiP up to 5.5ms since it does not consider the physical lengths of the virtual links. (5) Increase in power consumption (kw) DeMiP PoMiP DePoMiP Hours (EST) Figure 3. Increase in power consumption by DePoMiP and its predecessors. Total utilized channels DeMiP 180 PoMiP DePoMiP Hours (EST) Figure 4. Channel utilization of DePoMiP and its predecessors. Furthermore, PoMiP does not consider the physical lightpath lengths in its cost assignment functions. By jointly considering physical distances and power consumption, DePoMiP mitigates the propagation delay drawbacks introduced by PoMiP. As seen in Figures 5.a-c, DePoMiP can make a compromise between delay and power savings as it introduces only a few microseconds of increase in the propagation delay of each traffic type when compared to DeMiP. Finally, we study DePoMiP in terms of power consumption fairness among different parts of the cloud network. To this end, we adopt Jain s fairness index to obtain Node Power Fairness Index (NPFI), i.e., NPFI = ( N i=1 P i) 2 /(N N i=1 P i 2 ). Table II illustrates the nodeby-node power consumption under DePoMiP followed by NPFI values for each timeslot. NPFI under DePoMiP is 0.993, which means that DePoMiP is able to distribute power consumption uniformly in the network although demand profile is heterogeneous due to having different time zones. V. CONCLUSION Energy savings in the transport network, as well as the data centers, are crucial in energy-efficient cloud computing. Previously, we have shown the trade-off between energysavings and propagation delay when reconfiguring the network topology based on forecasted demand profiles. In this paper, we have proposed a novel provisioning scheme in /12/$ IEEE

6 Table II NODE-BY-NODE POWER CONSUMPTION (KW) AND NPFI OF DEPOMIP (a) Hours Nodes N N N N N N N N N N N N N N NPFI (b) (c) Figure 5. Delay performances of DePoMiP and its predecessors (a) Regular, (b) Downstream DC, and (c) Upstream DC demands. order to address this trade-off and jointly minimize power consumption and propagation delay in the cloud network. The proposed scheme is called Delay and Power Minimized Provisioning (DePoMiP). DePoMiP re-designs the virtual cloud network by considering power consumption and propagation delay factors when provisioning the demands. Through simulations, we have shown that DePoMiP introduces minimized power consumption while introducing a few microseconds of additional propagation delay to delay minimized provisioning. Furthermore, we have shown that DePoMiP does not penalize the cloud network in terms of channel consumption or power consumption distribution. REFERENCES [1] P. Mell and T. Grance, The NIST Definition of Cloud Computing, Jan 2011, [Online] [2] Q. Zhang, L. Cheng, and R. Boutaba, Cloud computing: state-ofthe-art and research challenges, Journal of Internet Services and Applications, vol. 1, pp. 7 18, May [3] Y. Zhang, P. Chowdhury, M. Tornatore, and B. Mukherjee, Energy Efficiency in Telecom Optical Networks, IEEE Comm. Surveys and Tutorials, vol. 12/4, pp , [4] J. Baliga, R. Ayre, K. Hinton, and R. Tucker, Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport, Proc. of the IEEE, vol. 99/1, pp , [5] ENERGY-STAR Program, Report to congress on server and data center energy efficiency, Aug. 2007, [Online] [6] A. Banerjee, T. Mukherjee, G. Varsamopoulos, and S. K. S. Gupta, Integrating cooling awareness with thermal aware workload placement for HPC data centers, Sustainable Computing: Informatics and Sys., vol. 1/2, pp , [7] Q. Tang, S. K. S. Gupta, and G. Varsamopoulos, Energy- Efficient Thermal-Aware Task Scheduling for Homogeneous High- Performance Computing Data Centers: A Cyber-Physical Approach, IEEE Trans. on Parallel and Distributed Systems, vol. 19, pp , Nov [8] J. Moore, J. Chase, P. Ranganathan, and R. Sharma, Making scheduling cool: temperature-aware workload placement in data centers, in Proc. of USENIX Annual Tech. Conf. (ATEC), Apr 2005, pp [9] Q. Zhang, Q. Zhu, and R. Boutaba, Dynamic Resource Allocation for Spot Markets in Cloud Computing Environments, in Proc. of IEEE Intl. Conf. on Utility and Cloud Computing, Dec. 2011, pp [10] B. Kantarci and H. T. Mouftah, Optimal Reconfiguration of the Cloud Network for Maximum Energy Savings, in Proc. of Workshop on Cloud Computing Optimization (CCOPT), May 2012 (accepted). [11] X. Dong, T. El-Gorashi, and J. M. H. Elmirghani, Green IP over WDM networks with data centers, IEEE/OSA J. of Lightwave Technology, vol. 29/12, pp , June [12] N. Charbonneau and V. M. Vokkarane, Routing and Wavelength Assignment of Static Manycast Demands Over All-Optical Wavelength- Routed WDM Networks, J. Opt. Commun. Netw., vol. 2, no. 7, pp , Jul [13] J. Buysse, C. Cavdar, M. De Leenheer, B. Dhoedt, and C. Develder, Improving energy efficiency in optical cloud networks by exploiting anycast routing, in Proc. of Asia Comm. and Photonics Conf., Nov [14] B. Kantarci and H. T. Mouftah, Energy-efficient cloud services over wavelength-routed optical transport networks, in Proc. of IEEE GLOBECOM, Dec 2011, pp. SAC SAC [15] B. Kantarci and H. T. Mouftah, Energy-Efficient Demand Provisioning in the Cloud, in Proc. of Optical Fiber Communication Conference (OFC), Mar 2012, pp. OM2G.4.1 OM2G /12/$ IEEE