We focus on the problem of efficient communications over access networks with asymmetric bandwidth and capability. We propose a resource-constrained asymmetric redundancy elimination algorithm (RCARE) to leverage downlink bandwidth and receiver capability to accelerate the uplink data transfer. RCARE can be deployed on a client or a proxy. Different from existing asymmetric algorithms, RCARE uses a flexible matching mechanism to identify redundant data and allocates a small sender cache to absorb the high downlink traffic overhead. Compared to existing redundancy elimination algorithms, RCARE provides a scalable sender cache that is adaptive based on resource and performance. We evaluate RCARE with real traffic traces collected from multiple servers and a campus gateway. The trace-driven simulation results indicate that RCARE achieves higher goodput gains and reduces downlink traffic compared to existing asymmetric communication algorithms. We design an adaptation algorithm for resource-constrained senders sending multiple data streams. Our algorithm takes samples from data streams and predicts how to invest cache size on individual data streams to achieve maximal uplink goodput gain. The adaptation algorithm improves the goodput gain by up to 87% compared to the baseline. In first 10% of data streams (sorted by the optimal goodput gains), RCARE achieves up to 42% goodput gain on average.

We consider the problem of asymmetric communications, which are common in many access networks. We propose a new asymmetric communication algorithm, called CacheQuery, to leverage on the already deployed downlink bandwidth and receiver capability to accelerate the uplink data transfer from one or multiple senders to a receiver. The design of CacheQuery differs from all previous asymmetric communication algorithms in two ways: (i) CacheQuery supports more flexible matching mechanism to identify redundant packet payload and (ii) CacheQuery allocates a small sender cache to absorb the potentially high downlink traffic overhead incurred by asymmetric communications. The trace-driven simulations indicate that, compared to existing asymmetric communication algorithms, CacheQuery achieves higher uplink transfer speed, yet reduces downlink traffic overhead.

Asymmetric access networks with downlink bandwidth 10-1000 times higher than uplink bandwidth are common, and upgrading these links may not be economically feasible in many usage scenarios. In this paper, we design, implement, and evaluate a parameterized Asymmetric Communication Layer (ACL) protocol that capitalizes the otherwise idling downlink bandwidth to boost the uplink goodput. The ACL protocol is different from existing techniques such as caching, protocol-independent redundancy elimination, WAN optimization, and online compression algorithms, because the ACL protocol is the first concrete network protocol to increase uplink goodput using downlink bandwidth. We implement the ACL protocol in ns-2 simulator, and conduct extensive simulations using synthetic traffic traces. The simulation results show the potential of the ACL protocol: compared to plain TCP/IP, 40-90% uplink goodput gain is possible. Users can also trade the goodput gain for lower delivery delay, as low as 1 RTT, or lower downlink payload amount, as low as 2.13 times of the actual data size. We also conduct a trace-driven simulation using a one-week real traffic trace of a Web server. We observe 24% uplink goodput gain, which shows the practicality of the ACL protocol.

In this paper, we propose a new class of type-2 fuzzy sets: Hedge Algebraic Type-2 Fuzzy Sets - HaT2FS. The particular feature of HaT2FS is that the membership grades of each element are linguistic truth values of hedge algebras. We consider some important aspects of this class of type-2 fuzzy sets: operations on linguistic truth values including linguistic aggregation, meet and join; HaT2FSs representation by hedge union of k-level embedded HaT2FSs; and intersection, union and complement operations on HaT2FSs.