Seamless computing and service sharing in community networks have been gaining momentum due to the emerging technology of community network micro-clouds (CNMCs). However, running services in CNMCs can face enormous challenges such as the dynamic nature of micro-clouds, limited capacity of nodes and links, asymmetric quality of wireless links for services, deployment mod- els based on geographic singularities rather than network QoS, and etc. CNMCs have been increasingly used by network-intensive services that exchange significant amounts of data between the nodes on which they run, therefore the performance heavily relies on the available bandwidth resource in a network. This paper proposes a novel bandwidth-aware service placement algorithm which out- performs the current random placement adopted by Guifi.net. Our preliminary results show that the proposed algorithm consistently outperforms the current random placement adopted in Guifi.net by 35 regarding its bandwidth gain. More importantly, as the number of services increases, the gain tends to increase accordingly.
Choreo @cite_7 is a measurement-based method for placing applications in the cloud infrastructures to minimize an objective function such as application completion time. Choreo makes fast measurements of cloud networks using packet trains as well as other methods, profiles application network demands using a machine-learning algorithm, and places applications using a greedy heuristic, which in practice is much more efficient than finding an optimal solution. In @cite_15 the authors proposed an optimal allocation solution for ambient intelligence environments using tasks replication to avoid network performance degradation. Volley @cite_14 is a system that performs automatic data placement across geographically distributed datacenters of Microsoft. Volley analyzes the logs or requests using an iterative optimization algorithm based on data access patterns and client locations, and outputs migration recommendations back to the cloud service.
@cite_15 @cite_14 @cite_7
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Ambient Intelligence (AmI) is an IT concept by which mobile users shall be seamlessly supported in their everyday activities. This includes interactions with remote resources as well as with their current physical environment. We have developed the so-called Ad hoc Service Grid (ASG) infrastructure that supports the latter form of interactions. It allows operators to cover arbitrary locations with ambient services in a drop-and-deploy fashion. An ambient service may autonomously distribute (replicate and migrate) within an ASG network to optimize its availability, response times, and network usage. In this article, we propose a fully decentralized, dynamic, and adaptive service placement algorithm for AmI environments like the ASG. This algorithm achieves a coordinated global placement pattern that minimizes the communication costs without any central controller. It does not even require additional communication among the replicas. Moreover, placement patterns stabilize if no changes occur in the environment while replicas still retain their ability to adapt. Mechanisms for self-organized placement of services are very important for AmI environments in general since they allow for autonomous adaptations to dynamic changes and, thus, remove the need for manual (re)configuration of a running system. We present a detailed evaluation of the algorithm's performance and compare it with three other algorithms to show its competitiveness. Furthermore, we discuss how the desired self-organizing behavior emerges from the interactions of a few simple, local rules that govern the individual placement decisions. In order to do so, we give an in-depth analysis of a series of emergent effects that are not directly encoded into the placement algorithm but stem from its collective dynamics. As cloud services grow to span more and more globally distributed datacenters, there is an increasingly urgent need for automated mechanisms to place application data across these datacenters. This placement must deal with business constraints such as WAN bandwidth costs and datacenter capacity limits, while also minimizing user-perceived latency. The task of placement is further complicated by the issues of shared data, data inter-dependencies, application changes and user mobility. We document these challenges by analyzing month-long traces from Microsoft's Live Messenger and Live Mesh, two large-scale commercial cloud services. We present Volley, a system that addresses these challenges. Cloud services make use of Volley by submitting logs of datacenter requests. Volley analyzes the logs using an iterative optimization algorithm based on data access patterns and client locations, and outputs migration recommendations back to the cloud service. To scale to the data volumes of cloud service logs, Volley is designed to work in SCOPE [5], a scalable MapReduce-style platform; this allows Volley to perform over 400 machine-hours worth of computation in less than a day. We evaluate Volley on the month-long Live Mesh trace, and we find that, compared to a state-of-the-art heuristic that places data closest to the primary IP address that accesses it, Volley simultaneously reduces datacenter capacity skew by over 2×, reduces inter-datacenter traffic by over 1.8× and reduces 75th percentile user-latency by over 30 . Cloud computing infrastructures are increasingly being used by network-intensive applications that transfer significant amounts of data between the nodes on which they run. This paper shows that tenants can do a better job placing applications by understanding the underlying cloud network as well as the demands of the applications. To do so, tenants must be able to quickly and accurately measure the cloud network and profile their applications, and then use a network-aware placement method to place applications. This paper describes Choreo, a system that solves these problems. Our experiments measure Amazon's EC2 and Rackspace networks and use three weeks of network data from applications running on the HP Cloud network. We find that Choreo reduces application completion time by an average of 8 -14 (max improvement: 61 ) when applications are placed all at once, and 22 -43 (max improvement: 79 ) when they arrive in real-time, compared to alternative placement schemes.