Shared resource

About: Shared resource is a research topic. Over the lifetime, 7536 publications have been published within this topic receiving 123491 citations. The topic is also known as: network share.

Heterogeneous network file access

Abstract: A method for a client to access data files residing on a first data server through a network includes coupling a heterogenous proxy server to the first data server through a first local network protocol, selectively receiving at the heterogeneous proxy server a data file from the first data servers by employing the first local network protocol, translating the data file into a format compatible with transmission through the network, and transmitting the translated data file to the client across the network.

System, method, and program for implementing priority inheritance in an operating system

Abstract: Disclosed is a method, system, and program for implementing priority inheritance in an operating system. A first list includes a boost request to boost a priority of an indicated thread holding a shared resource to an indicated priority. Further included is a second list. The system processes the boost request in the first list to boost the priority of the indicated thread to the indicated priority. After boosting priority, the system removes the processed boost request from the first list and places the processed boost request in the second list.

The Elasticity and Plasticity in Semi-Containerized Co-locating Cloud Workload: a View from Alibaba Trace

Abstract: Cloud computing with large-scale datacenters provides great convenience and cost-efficiency for end users. However, the resource utilization of cloud datacenters is very low, which wastes a huge amount of infrastructure investment and energy to operate. To improve resource utilization, cloud providers usually co-locate workloads of different types on shared resources. However, resource sharing makes the quality of service (QoS) unguaranteed. In fact, improving resource utilization (IRU) and guaranteeing QoS at the same time in cloud has been a dilemma which we name an IRU-QoS curse. To tackle this issue, characterizing the workloads from real production cloud computing platforms is extremely important. In this work, we analyze a recently released 24-hour trace dataset from a production cluster in Alibaba. We reveal three key findings which are significantly different from those from the Google trace. First, each online service runs in a container while batch jobs run on physical servers. Further, they are concurrently managed by two different schedulers and co-located on same servers, which we call semi-containerized co-location. Second, batch instances largely use the spare resources that containers reserved but not used, which shows the elasticity feature of resource allocation of the Alibaba cluster. Moreover, through resource overprovisioning, overbooking, and overcommitment, the resource allocation of the Alibaba cluster achieves high elasticity. Third, as the high elasticity may hurt the performance of co-located online services, the Alibaba cluster sets bounds of resources used by batch tasks to guarantee the steady performance of both online services and batch tasks, which we call plasticity of resource allocation.

Multi-service load sharing for resource management in the cellular/WLAN integrated network

Abstract: With the interworking between a cellular network and wireless local area networks (WLANs), an essential aspect of resource management is taking advantage of the overlay network structure to efficiently share the multi-service traffic load between the interworked systems. In this study, we propose a new load sharing scheme for voice and elastic data services in a cellular/WLAN integrated network. Admission control and dynamic vertical handoff are applied to pool the free bandwidths of the two systems to effectively serve elastic data traffic and improve the multiplexing gain. To further combat the cell bandwidth limitation, data calls in the cell are served under an efficient service discipline, referred to as shortest remaining processing time (SRPT). The SRPT can well exploit the heavy-tailedness of data call size to improve the resource utilization. An accurate analytical model is developed to determine an appropriate size threshold so that data calls are properly distributed to the integrated cell and WLAN, taking into account the load conditions and traffic characteristics. It is observed from extensive simulation and numerical analysis that the new scheme significantly improves the overall system performance.