Project topic: Adaptive cloud based services for mobile users Paper to present: Competitive Analysis for Service Migration in VNets Zahra Abbasi

Introduction to the project Assumptions: Providing service for mobile users through clouds Cloud based services: Couple of DCs that are networked Infrastructure of the network and DC are hidden from service provider and users Service can be hosted in any DC of the cloud The access point of mobile users changes over time Adaptive cloud based services Dynamically changing the number and the locations of virtual servers to: Minimizing energy consumption Maximizing quality of service for mobile users Modeling the problem as an optimization problem Simulation based evaluation

Competitive Analysis for Service Migration in VNets Marcin Bienkowski Anja Feldmann Dan Jurca Wolfgang Kellerer Gregor Schaffrath Stefan Schmid and Joerg Widmer University of Wrocław, Poland, docomolab-euro.com And T-Labs / TU Berlin Berlin, Germany ACM SIGCOMM 2010

Introduction-Motivation Virtualized network/Cloud computing The detail of infrastructure is hidden for service providers and users Applications can be hosted in any node in a dynamic fashion

Introduction-Motivation Virtualized network/Cloud computing The detail of infrastructure is hidden for service providers and users Applications can be hosted in any node in a dynamic fashion Potential advantageous for mobile users Improving the quality of service by dynamic service migration Service migration management Migration cost: Service outage, migration cost Service cost: Delay of requests Research question: To migrate or not to migrate? (online) How to compare with the offline optimal solution?

Overview on contributions and results Proposing an online service migration for mobile based services Competitive analysis and deriving the competitive ratio (log n) Online vs. offline Offline: All access point information of users is known in advance Online: The past and current information is available

System model and assumptions Virtualized network G=(V,E) A bandwidth is associated with any edge of the set E Service can be hosted in any node of the set V Access cost of users Number of hops in the shortest path from the access point to the server Migration cost Costacc(A)=2 A Bw=10 Bw=10 Costmig(S2,S3,size(s)=100)=10

System model and assumptions The system makes decision for the migration on time slots called rounds Requests access points changes at rounds Access points: t0={A,B}, t1={C,D} where {A,B,C,D} are nodes of the set V. There is only one service

Online algorithm Strike balance between Costacc and Costmig Given an initial physical location of the server V0 ∈ V An initial access point set of requests ⊆ V Phase: multiple of rounds Step 1: Migrate to v’ if Lv >= β, where v’ is randomly chosen among nodes whose Lv’ >= β Reset Lv if all Lv >= β End of phase

Online Alg. Example Phase L(A) L(B) L(C) L(D) L(E) β Tcost 0+5=5 2+3=5 acc(B)+acc(D) =1+6=7 0+5=5 2+3=5 5+0=5 7+2=9 100/10=10 7(A) 7+4+8=19 5+3+7=15 5+0+4=9 5+2+2=9 9+4=13 10 7+10+4=21(C) 21+2=23(C) 19+4+6=29 15+3+5=23 9+0+2=11 9+2+0=11 13+4+2=19 10 10 23(C)

Optimal versus online -example BW: 10, Latency: 11, size(s)=100 t0: {C}, v0=B t1:{B} Total cost for online alg: 20 Total cost for optimal: 11 B C A

Competitive analysis For a given phase: Cost(OPT) >= β Case1:If the optimal solution does a migration then: Cost(OPT) >= β Case 2: If it does not migration OPT pays L(v) for a fixed v during a phase , where L(v) >= β Expected number of migration for the ALG is at most Hn (nth harmonic number, O(Hn)=logn) {V}: the descending ordered set of vertices whose L(v) reaches β Prev Ex: v1=A, v2=B, v3=E, v4=C, v5=D Ti: expected # of migration given Vi as the initial point: Recursive relationship for any vi and vj j>=i: Ti=1+Tj where j>i

Conclusion Extending the modeling to our problem Simplification of assumption to derive the competitive ratio Extension of the model for the cloud based mobile services Energy cost of centers are taken into account Servers are allowed to be duplicated

The Case for VM-based Cloudlets in Mobile Computing Mahadev Satyanarayanan, Paramvir Bahl Ramon Caceres, Nigel Davies Since battery life, size and weight has more priority, mobiles can not provide the desired computational power needed for resource-intensive applications such as applications for augmenting human cognition. Both computation and energy is the intrinsic limitation of mobiles for state of the art environment interpretation applications. Far more advanced than logical localization That can be done using mobile resources.

Introduction Cloud computing is a solution for resource- poverty of mobiles. Architecture for Virtual Machine Provisioning Customized Service nearby Cloud Computing Limitations Cloudlet Approach Proof-Of-Concept Challenges Web-based application services over the Internet These applications that run poorly in mobiles, need cloud Mobile device can execute a resource-intensive applications on a distant high performance computer server or computer cluster.

Cloud Service for Mobiles Battery, Weight, and Size are the most priority of mobile manufacturers Virtualization to face resource poverty is needed Human cognitive applications Facial/speech recognition Scene interpretation Voice synthesis/translation Computational intensive applications Internet Delay and Jitter are harmful for interactive/real-time applications Helps elder or disable Needs High performance computation on a large amount of data

Cloud Computing Limitations Delay can damage acuteness of graphic rendering interactive applications. To face delay applications reduce the resolution or frame rate This slide shows the user perception quality is variable and depends on the end-to-end latency. In a tested photo editing application the response for more than 150 ms is noticeable and annoying Even if the response time is 100ms, 30% of interaction are noticeable to the user.

Future of Cloud Computing for Mobiles Using Wireless LAN More Bandwidth Less Delay/Latency, Less Jitter No rely on distant cloud Local Data Centers Cloudlets Wireless access point PC / Computer Cluster Internet Access We can benefit cloud computing without WAN limited, rather than relying on distant cloud. The resource poverty can be addressed by using a nearby resource-rich cloudlet

Cloudlet: Tiny cloud nearby On-hop wireless LAN, more bandwidth Provide real-time response time , low delay Consume less energy, more green Wide spread, decentralized, more ubiquitous Self-managed, easily setup, more chip Can work connection less, independent from Internet 1- Provide Better for local Services One-hop wireless access to the cloudlet Physical proximity of cloudlet meets the fast response time Data Center in a Box

Cloudlet State Diagram The first step of using cloudlet is to search the cloudlet in the local access point. If there is a cloudlet, mobile will interact with it. If there is not any cloud let with proximity, mobile searches for the distant cloud over the internet. The worst case will occur when the mobile has to use its own resources

Cloudlet customization Customized VM transiently by mobiles Mobile Clients: Pre-use customization Use service Post-use cleanup Dynamic Virtual Machine Synthesis VM base in cloudlet VM overlay as service application Lunch VM on cloudlet To support the widespread of cloudlets, Cloudlets should support widest possible mobile user with any OS or any applications. Therefore the best way is to use them as a VM infrastructure. Mobiles should customize them transiently for their specific application Post-use cleanup is really needed to

Dynamic VM Synthesis State Diagram Base VM Install the Overlay VM Lunch VM Overlay VM In Dynamic VM synthesis, a small overlay delivered by a mobile to cloudlet infrastructure Base virtual machine VM Residue Done

Proof-of-Concept (Kimberly) Kimberly uses virtual box, which is a VMM on a Linux Ubuntu VM base is a minimally configured guest OS which is compatible with mobile devices. 1- Lunch VM base on virtual box (kimberlize) 2- Guest executes install-script to install overlay VM 3- Executes resume-script to lunch VM over the cloudlet 4- The control manager of the transient binding between mobile device and the cloudlet is a process which is called Kimberly Control Manager (They abstract Service discovery and Network Management) Avahi is linux-based tool for DNS, Service discovery, and Network Management Communication will be based on secure TCP tunnel using SSL between KCM instances. Dekimberlize will fetch, decrypts, decompress and apply overlay to base VM

KCM VM Synthesis Delay Mobile users will face extended delays for service instantiations Kimberly is an unoptimized initial prototype Synthesizing a VM in 60-90 seconds.

Cloudlet Challenges Initiation Delay Business Model Size Trust & Security Migration & Handout 1- Since the mobile should install the overlay on the cloudlet cloudlets may have initiation problems 2- The second challenge is whether the business owners provide cloudlet or service providers use share the profits with the retail business. 3- Whether to use PC, server or cluster in the local areas. 4- To prevent malicious applications, users, VMM, there are 2 approaches: trust establishment, that users should perform some pre-use action before service reputation-based trust which is based on the identity of business 5- Which is to locate the service based on the user mobility

Conclusion New approach of Cloud Computing for Mobiles Nearby resource-rich computers High Bandwidth and Low Latency Good for Local Applications Investment & Infrastructure Business & Marketing Mobiles utilize the nearby resource-rich computers Cloudlets are good for applications that use local data Cloudlets need marketing to convince local business Using Cloudlet on a campus does not need any investment

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