International Journal of Research In Science & Engineering e-issn: Special Issue: Techno-Xtreme 16 p-issn:

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1 Cloudlet Scheduling To Optimize Cloud Computing With Wireless Sensor Network Miss. P.P.Ingale, Prof. R. N. Khobragade, Dr. V. M. Thakare Dept. CS & IT, SGBAU, Amravati, India. Dept. CS & IT, SGBAU, Amravati, India. Dept. CS & IT,SGBAU, Amravati, Abstract: Connecting mobile devices with the cloud, suffers from high network latency and the huge transmission power consumption. Main obstacle for today s mobile devices is lack of resource, and requires increasing the processing power of device, increasing memory capacity, a need for greater durability of the battery etc. Cloud computing is one of the most recently developed models of network computing that provide services over network on demands. cloud are highly adapted in present day computing but real issues regarding performance, reliability, and security still exist in such complex systems. One of the main issues is regarding the pay-per-use concept of the clouds that may be affected by underutilization of the reserved resources by a single user. So even the cloud suffers from underutilization but per user s reserved resources, not per data center as a whole. Therefore, the maximization of system utilization while simultaneously minimizing makespans is of great interest to cloud users wishing to reduce usage costs through the decrease of usage time. This paper proposes, a cloudlet scheduling method inside the CloudSim able to maximize the resource utilization and minimize the makespan. Additionally Localization method optimizes the power consumption during a trilateration-based localization procedure through the adjustment of sensor node s output power level. Keywords: cloudlet scheduling, CloudSim, Localization, makespans, resource utilization. 1. Introduction: Mobile devices such as tablets, smart phones have become an essential part of day to day life. Users depends on them to make calls, access online social networking sits, create and edit documents, organizing meetings and make video/audio calls, perform image processing operations and so on. But the main obstacle for today s mobile device are lack of resources, and they requires increasing the processing power of device, increasing memory capacity, a need for greater durability of the battery etc. One solution to overcome these problems is to integrate cloud computing technology with mobile devices which is called as mobile cloud computing. This paper introduces cloudlet scheduling method by using CloudSim which is able to maximize the resource utilization. Problem of using the cloud is the under utilization of the reserved resources, which causes longer makespans and higher usage cost. Also, the optimization of sensor node s power consumption, WSN, is very critical due to the fact that sensor nodes are small in size and have constrained resources in terms of power/energy, connectivity and computational power. This paper concern on how cloud computing system with WSNs can take advantages of computational intelligent (CI) techniques using single-or-multi-objective particle swarm optimization (PSO), with an overall aim of concurrently minimizing makespans, localized time, energy consumption during localization and maximizing the number of nodes fully localized. Two optimization methods are applied to the Cloud scheduling; in order to decrease the execution time of tasks (Cloudlets) on the cloud, and to WSN localization problem; in order to find optimal solutions while optimizing the power consumption of the whole network. 2. Background: The study of mobile cloud computing discusses the most relevant mobile cloud computing and cloudlets techniques developed in recent years with their challenges. The MCC concept with the proposed Cloudlet framework are integrated and a new scalable framework for the MCC model is proposed which reduces the power consumption of the mobile devices as well as reducing the communication latency when the mobile device request a job to be performed remotely while satisfying the high quality of service requirements. In the cloudlet-based mobile computing, mobile devices send jobs to the cloudlet to perform the required processing and return the final result back, this reduces the transmission delay, also reduces the power consumption of the mobile device. So it makes a great evolution in MCC [1]. Discussions on cloudlet challenges explained the importance of using Cloudlets in our everyday life, by presenting the main challenges and possibilities of further research. Also presented a short introduction about Cloudlets; the cloudlets architecture and the way they operate and discuss about what their features are, how they work with thin and tick clients, and how they operate as offload element [2].

2 PacketCloud: a Cloudlet-based open platform for in network services, which shares in network services, while satisfies a number of practical requirements to obtain result of efficiently share a set of commodity servers among different services, and serve the network traffic in an elastic way. A cloudlet is a general-purpose cluster consisting of a set of commodity servers, and it can host in-network services. In each cloudlet, commodities server can be efficiently shared among different services, and accordingly achieve an elastic resource allocation for each deployed service. The use of PacketCloud will provide a cost-effective replacement of existing middleboxes, also get third-party application/content provider close to end users. Different from specialized middleboxes, available resources are required to be shared among different services. To serve the highly dynamic network traffic, PacketCloud is designed to host elastic in network services [3]. PacketCloud, a cloudlet-based open platform to host elastic in-network services, discussed how PacketCloud can help both Internet Service Providers (ISPs) and emerging application/content providers deploy their services to strategic network locations [4]. Metropolitan areas have a high population density, meaning that cloudlets will be accessible to a large number of users. This improves the cost-effectiveness of cloudlets as they are less likely to be idle. Also due to the size of the network, WMAN service providers can take advantage of economies of scale when offering cloudlet services through the WMAN, making cloudlet services more affordable to the general public [5]. This paper used CloudSim to implement cloudlet scheduling method. CloudSim is a simulator tool that allows testing the performance of provisioning policies in a repeatable and controllable environment. CloudSim is a library for the simulation of cloud scenarios. It provides essential classes for describing data centers, computational resources, virtual machines, applications, users, and policies for management of various parts of system like scheduling and provisioning. This Paper organized as follows Section 1 Introduction. Section 2 discusses Background. Section 3 discusses previous work done. Section 4 discusses existing methodology. Section 5 discusses analysis and discussion. Section 6 proposed method Section 7 outcome possible result. Finally section 8 concludes this review paper and Section 9 related future works. 3. Previous Work Done: Author Yaser Jararweha et al. (2014) [1] proposed a new MCC cloudlet-based model is composed of a set of distributed and well connected cloudlets within one location where most mobiles use cloud services. All of these cloudlets connected to the Enterprise remote cloud. The mobile device directly communicates with the cloudlet which is connected to the Enterprise cloud. In some cases, the mobile device will need to connect to the enterprise cloud, even if the cloudlets are available. In one case, the device need to access a file stored in the Enterprise Cloud. In another case, the mobile device will need to access services that are not available in the Cloudlet. Author Aleksandar Bahtovski et al. (2014) [2] proposed the cloudlet architecture. This can be explained as a high level architecture for code offloading in a hostile environment. Cloudlets are the middle element in a threetier architecture i.e. intermediate layer between the cloud infrastructure and the mobile device. The other end of the architecture includes offload elements for mobile device cloudlet. They are located close to the mobile devices they serve. This architecture decreases latency by using a single-hop network and potentially lowers the battery consumption by using Wi-Fi or short-range radio instead of broadband wireless, which typically consumes more energy. The usage of VM (Virtual Machine) in cloudlets enables clean separation. The complex problem of configuring software on the cloudlet to service mobile devices is avoided. Instead, the problem is transformed into a simpler problem of rapidly delivering a precisely preconfigured VM to the cloudlet. A VM cleanly encapsulates and separates a transient guest software environment from the permanent host software environment of the cloudlet infrastructure. In a hostile environment, efficient dissemination of VM to the cloudlet is a major challenge. Author Yang Chen et al. (2015) (2013) [3,4] proposed a cloudlet is a functional unit in the resource pool to exchange data with the Internet; every cloudlet has a co-located front-end router (or switch). The router checks against every packet, and see if it is related to any deployed in-network service on the co-located cloudlet. If so, the packet will be forwarded to the cloudlet for further processing. A cloudlet is composed of a cloudlet controller, and a number of commodity servers, called computation nodes. The cloudlet controller is a general purpose server, and is responsible for the resource allocation and management of all nodes within the cloudlet. The cloudlet controller actively monitors the resources of every computation node, and reports to the ISP-wide resource scheduler periodically. The computation nodes are used for hosting in-network services. Virtualization is necessary to provide isolation among them, and a computation node can host multiple Virtual instances (VIs) simultaneously. PacketCloud supports multiple types of VIs. Each type has a specified resource capacity. A VI can host general purpose in-network services subject to its resource constraint. Author Mike Jia et al. (2015)[5] proposed the strategic placement of a limited number of cloudlets in a WMAN (a wireless metropolitan area network) can significantly improve the performance of mobile user applications

3 and device an algorithm for the problem, which enables the placement of the cloudlets at user dense regions of the WMAN, and assigns mobile users to the placed cloudlets while balancing their workload. 4. Existing methodology: A new MCC cloudlet-based model is composed of a set of distributed and well connected cloudlets within one location where most mobiles use cloud services. All of these cloudlets are connected to the Enterprise remote cloud. The mobile device directly communicates with the cloudle. In some cases, the mobile device will need to connect to the enterprise cloud, even if the cloudlets are available. In one case, the device need to access a file stored in the Enterprise Cloud. In another case, the mobile device will need to access services that are not available in the Cloudlet [1]. In cloudlet architecture Cloudlet are the middle element in a three-tier architecture i.e. intermediate layer between the cloud infrastructure and the mobile device. The heart of this architecture is a large central core, which can be implemented as one of the Amazon s, Microsoft s, Google s data centres or private enterprise clouds. The other end of the architecture includes offload elements for mobile devices cloudlets. They are located close to the mobile devices they serve [2]. In PacketCloud, the small size of the cloudlets allows ISPs to deploy in-network services in a number of network locations, e.g., PoPs (point of presences). Even a small ISP can deploy multiple cloudlets in its PoPs. PacketCloud allows deployed in-network services to be activated in two different ways. A service provider can let the public know the network address of a deployed service. These types of services are denoted as visible services. On the other hand, a service provider can choose to sniff or even modify the traffic on-the-fly, without the awareness of end users. This type of services are invisible to end users, known as transparent services [3][4]. A simple Heaviest-AP First (HAF) algorithm and A Density-Based Clustering (DBC) algorithm, those two heuristic algorithms are used for the K cloudlet placement problem and user-to-cloudlet assignment in a WMAN, to reduce the average wait time of offloaded task [5]. PSO is a population-based search algorithm, where each particle learns from its neighbors and itself during the time it travels in space. PSO algorithm start by creating a number of particles to form a swarm that travels in the problem space searching for an optimum solution. An objective function should be defined to examine every solution found by each particle throughout the traveling time. The individuals in PSO are a group of particles that move through a search space with a given velocity [6]. Equations 1 to 3 are used to update the velocity of the ith component of particle, where 4 and 5 are used to update the position of that same component. (1) (2) (3) (4) p = (5) PSO will initialize swam of particles where each particle will have a velocity and positions vector, the complete pseudo code is shown in Algorithm. 1.

4 Algorithm 1: BSOPSO pseudo code 5. Analysis and discussions: In the cloudlet-based mobile computing, mobile devices send jobs to the cloudlet to perform the required processing and return the final result back, this reduces the transmission delay, also reduces the power consumption of the mobile device. Using cloudlets would be of great benefit for mobile service customers, especially if they want to use an application that requires resources that the customer s mobile device doesn t have at the moment. To serve the highly dynamic network traffic, PacketCloud is designed to host elastic in network services. As an open platform, security is very important as deployed services might be unstable, or even malicious. PacketCloud must be robust to different kinds of service failures and malicious attacks. packetcloud introduce a small additional delay for in network services. Although PacketCloud is designed for handling high throughput traffic, still, a cloudlet s processing capability is not as high as a public data center. PacketCloud is able to host elastic in-network services to serve the dynamic Internet traffic. There are several issues need to consider in the design those are unexpected service failures, malicious demultiplexing rules, malicious services and Excessive resource usage. Due to the size of the network, WMAN service providers can take advantage of economies of scale when offering cloudlet services through the WMAN, making cloudlet services more affordable to the general public. However, if consider the use of cloudlets in Wireless Metropolitan Area Networks (WMANs), the problem of placement becomes much more significant. Following table summarizes discussions of above methods. Methods Advantages Disadvantages Cloudlet-base mobile computing Reduce transmission delay during processing and return of result back. Reduce power consumption. Cloudlet architecture PacketCloud PacketCloud Cloudlets WMAN in Ability to work with thin and tick client. Operated as offload element. architecture decreases latency by using a single-hop network and potentially lowers the battery consumption by using Wi-Fi or Short-range radio instead of broadband wireless. Serve highly dynamic network traffic. Designed to host elastic in network services. Robust to different kinds of service failures and malicious attacks. Help both internet service provider (ISPs) and emerging application/content providers. Compatible with different underlying network architecture including today s internet protocol. Making cloudlet services more affordable to the general public. High population density. Required large network to build three architectural model and using real testbed. Architecture wants to use an application that requires resources that the customers mobile device doesn t have at that time. Cloudlet s processing capability is not as high as a public data center. Unexpected service failures, Malicious demultiplexing rules, Malicious services and Excessive resource usage. Unexpected service failures, Malicious demultiplexing rules, Malicious services and Excessive resource usage. The problem of placement becomes much more significant. In HAF the APs with the heaviest workload are not always the closest ones to their users. And lead to uneven distribution of users to cloudlet. In DBC algorithm there is issue how to prioritize candidate users for assignment to the cloudlet

5 Table1: Comparative analysis of various cloudlet methods. 6. Proposed Methodology: Optimization algorithm is truly complex procedures that consider many elements when optimizing a specific problem. Cloud computing and wireless sensor networks are full of optimization problem that need to be solved. Cloudlet scheduling with cloud computing system and WSNs proposed to take advantages of computational intelligent techniques using single-or-multi-objective particle swarm optimization, with an overall aim of concurrently minimizing makespans, localized time, and energy consumption during localization and maximizing the number of nodes fully localized. A cloudlet scheduling inside the CloudSim is able to maximize the resource utilization and minimize the makespan. Additionally the localization method optimized the power consumption during a trilateration-based localization procedure through the adjustment of sensor node s output power level. Makespan, Utilization, and Scheduling: Makespan is the total time the network resources take to finish executing all the tasks or jobs, and utilization is the measure of how well the overall capacity of the cloud is used. Cloudlet Scheduling: scheduling in CloudSim is done at the node level; there are two different scheduling algorithms in two different levels of the software: the VM level and the host level. The provisioning scenarios use space-shared and time-shared policies for VMs and task units. Using the space-shared policy allows one VM or cloudlet to be executed at a given moment of time. On the contrary, using the time-shared policy allows multiple cloudlets to multi-task within a VM and will allow VMs to multi-task and run simultaneously within a host, in case time-shared was used in the two aforementioned levels. VMs will be allocated to hosts on a FCFS basis. The simple brokering will iterate through all cloudlets then assign them to the available VMs one by one. For example, if we have two VMs and three cloudlets, the broker will assign the first VM to run the first cloudlet, the second VM to run the second cloudlet, and then will start again with the first VM by assigning it to run the third cloudlet. CloudSim Network Topology: CloudSim tool starts by creating a network of nodes as illustrated in Figure 1. A basic node consists of data centers, hosts, and cloud brokers. Data centers (resource providers in CloudSim) are created first with specifications that define the operating system, memory, bandwidth, storage, etc. One or more hosts are then created on each data center with the proper specification of RAM, storage, bandwidth, processing elements, and the selection of the scheduling algorithm to schedule virtual machines (VMs) inside the host. Processing elements are known as cores or CPUs, where each processing element is given a defined processing power measured in millions of instructions per second (MIPS). Hosts are managed by data centers where each data center may manage a single or numerous hosts. The cloud broker, \an entity that creates and maintains relationship with multiple cloud service providers", is also created to distribute work among the available data centers or cloud services, which make a cloud, broker the middleware subsystem in charge of the relationship between users and cloud service providers. VM1 Data Center 1 Host 1 VM1 VM Cloudlet 1 Cloudlet n Cloudlet Host X VM y Cloudlet Z VM Scheduling Cloudlet Scheduling

6 Figure 1: CloudSim Network Topology After creating all of the network nodes of CloudSim, VMs are created in order to run on the specified hosts. A scheduling algorithm should be chosen to schedule cloudlets inside the VM, so scheduling is done at the host and VM levels. The last step in this process is the generation of tasks (aka. cloudlets) either by initializing them through code or from existing workload traces. Cloudlets are defined based on specifications that define the task length in millions of instructions (MI), needed number of processing elements, and a utilization model that states the cloudlet's execution rate through defining the current requested MIPS from the processing elements. When generating cloudlets from workload traces, the workload format should be checked to make sure that it follows the standard workload format described. Cloudlets' length should also be converted to MI instead of the standard's execution time in seconds, and that is achieved by multiplying the execution time by the execution rate, where the default execution rate in CloudSim is 1 MIPS. For example, a cloudlet with an execution time of 10 seconds is converted to 10 MI. Finally, after creating network nodes, VMs, and cloudlets, the list of the available VMs and cloudlets are submitted to the cloud broker to assign each cloudlet to run on a specific VM based on the brokering policy. Wireless Sensor Network: Trilateration-based and multilateration-based localization (TBL & MBL) techniques are among the best known and most used methods for localization. WSNs are use to reduce the overall power consumption of the localization process without affecting the localization time or localizability (i.e. the number of localized nodes). Each node in the WSN can eventually be localized with the help of three already localized neighbor nodes that a node can communicate with over 1-hop connections. Two nodes are said to have a 1-hop connection if the distance between them is less than or equal to the transmission range, R. The localization procedure is the step that precedes actual network transmissions which, in the long run, will help in data forwarding and routing procedures between nodes in the network. The proposed method involves the use of single and multi-objective PSO to choose the appropriate, discrete or continuous output power level for each wireless sensor node. PSO was used in order to optimize various single or combinations of objectives including localization time, messages sent during localization, and the power consumed. 7. Outcomes And Possible Result: Cloudlet scheduling with PSO advancing work of the brokers, which will be maximize the resource utilization and minimize the makespans. 8. Conclusion: The study of mobile cloud computing discusses the most relevant mobile cloud computing and cloudlets techniques developed in recent years with their challenges. Connecting mobile devices with the cloud suffers from the high network latency and the huge transmission power consumption and lack of resources, and they requires increasing the processing power of devices, increasing memory capacity, a need for greater durability of the battery etc. A cloudlet scheduling method inside the CloudSim is able to maximize the resource utilization and minimize the makespan. Additionally the localization methods optimized the power consumption during a trilateration-based localization procedure through the adjustment of sensor node s output power level. 9. Future Scope: In future Different CI methods can be implemented; then, the performance of them can be tested and compared with the performance and quality of the solutions by the PSO-based variants. The cloudlet scheduling method showed great opportunity for optimizing single user makespans. However, a more complex and realistic virtualized cloud environment can be created to test the method with multi-user and real cloudlets. The work can be further extended by implementing different localization protocols. References: [1]Yaser Jararweha, Lo'ai Tawalbehb, Fadi Ababneha, Abdallah Khreishahc*, Fahd Dosari Scalable Cloudletbased Mobile Computing Model Science direct(mobispc-2014) Page no [2]Aleksandar Bahtovski, Marjan Gusev, Cloudlet Challenges volume no. 69, page no ScienceDirect 2014.

7 [3] Yang Chen, Member, IEEE, Yu Chen, Qiang Cao, and Xiaowei Yang PacketCloud: a Cloudlet-based Open Platform for In-network Services IEEE transactions on parallel and distributed systems vol. pp. no. 99. page no April 2015 [4]Yang Chen1, Bingyang Liu, Yu Chen1, Ang Li, Xiaowei Yang, Jun Bi PacketCloud: an Open PlatformforElasticInnetworkService MobiArch 13,October4,2013,Miami,Florida,USA copyright 2013 ACM. [5]Mike Jia, Jiannong Cao, Fellow, IEEE, and Weifa Liang Senior Member, IEEE Optimal Cloudlet Placement and User to Cloudlet Allocation in Wireless Metropolitan Area Networks IEEE transactions on cloud computing, vol. x, no. x, xx [6] Book: Artificial Intelligence and Intelligent System by N. P. Padhy. OXFORD edition, page no