Probabilistic Best-Fit Multi-dimensional Range Query in Self-Organizing Cloud

Abstract

Abstract-With virtual machine (VM) technology being increasingly mature, computing resources in modern Cloud systems can be partitioned in fine granularity and allocated on demand with "pay-as-you-go" model. In this work, we study the resource query and allocation problems in a SelfOrganizing Cloud (SOC), where host machines are connected by a peer-to-peer (P2P) overlay network on the Internet. To run a user task in SOC, the requester needs to perform a multi-dimensional range search over the P2P network for locating host machines that satisfy its minimal demand on each type of resources. The multi-dimensional range search problem is known to be challenging as contentions along multiple dimensions could happen in the presence of the uncoordinated analogous queries. Moreover, low resource matching rate may happen while restricting query delay and network traffic. We design a novel resource discovery protocol, namely Proactive Index Diffusion CAN (PID-CAN), which can proactively diffuse resource indexes over the nodes and randomly route query messages among them. Such a protocol is especially suitable for the range query that needs to maximize its best-fit resource shares under possible competition along multiple resource dimensions. Via simulation, we show that PID-CAN could keep stable and optimized searching performance with low query delay and traffic overhead, for various test cases under different distributions of query ranges and competition degrees. It also performs satisfactorily in dynamic node-churning situation.

Social-optimized win-win resource allocation for Self-organizing Cloud

“…4, respectively. We could observe that the throughput of SocialOPT is no worse than that of the randomselection design in our previous work [14], no matter for the scheduled tasks or finished tasks. Fig.…”

“…As a comparative baseline, we also implement another nodeselection strategy, namely random-selection strategy, which was adopted in our previous work [14]. Under such a strategy randomly, each scheduler node selects one execution node from the qualified candidate resources queried by PID-CAN protocol [14], in order to alleviate the decision conflict among the resource requesters.…”

“…Under such a strategy randomly, each scheduler node selects one execution node from the qualified candidate resources queried by PID-CAN protocol [14], in order to alleviate the decision conflict among the resource requesters. This random-selection design also delivers satisfactory throughput based on our previous work [14], but with degraded contributors' payoffs observed in this paper, easily mitigating their participating motivations.…”

“…Then, we carefully constructed the CAN protocol [16] using the Peersim tool [19] and improved it using our designed Proactive-Index-Diffusion (PID) strategy [14]. There are thousands of nodes, each with random settings (Table I).…”

“…Nodes' availability states will be dynamically propagated using the multidimensional resource discovery protocol, namely Proactive Index Diffusion CAN (PID-CAN), which appears in our previous work [14], in order to effectively discover available resources for submitted tasks (i.e. Step 1 shown in Fig.…”